Free space detection is a very important task in road scene understanding. With the continued development of convolutional neural networks, free-space can be seen as class-specific semantic segmentation problem. In this paper, new encoding–decoding network structure-HRUnet designed, which always maintains input high-resolution images both encoding and decoding phases. It extracts multi-scale in...

Active debris removal and unmanned on-orbit servicing missions have gained interest in the last few years, along with possibility to perform them through use of an autonomous chasing spacecraft. In this work, new resources are proposed aid implementation guidance, navigation, control algorithms for satellites devoted inspection noncooperative targets before any proximity operation is initiated....

Feature selection can significantly be decisive when analyzing high dimensional data, especially with a small number of samples. Feature extraction methods do not have decent performance in these conditions. With small sample sets and high dimensional data, exploring a large search space and learning from insufficient samples becomes extremely hard. As a result, neural networks and clustering a...

Over the last decade of machine learning, convolutional neural networks have been most striking successes for feature extraction rich sensory and high-dimensional data. While learning data representations via convolutions is already well studied efficiently implemented in various deep libraries, one often faces limited memory capacity insufficient number training data, especially large-scale ta...